A Hybrid Belief Rule-Based Classification System Based on Uncertain Training Data and Expert Knowledge

Lianmeng Jiao (1, 2) , Thierry Denoeux (3, 1) , Quan Pan (2)

1 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]
2 School of Automation,Northwestern Polytechnical University Xi'an, China.
3 Labex MS2T - Laboratoire d'Excellence "Maîtrise des Systèmes de Systèmes Technologiques"

In some real-world classification applications, such as target recognition, both training data collected by sensors and expert knowledge may be available. These two types of information are usually independent and complementary, and both are useful for classification. In this paper, a hybrid belief rule-based classification system (HBRBCS) is developed to make joint use of these two types of information. The belief rule structure, which is capable of capturing fuzzy, imprecise, and incomplete causal relationships, is used as the common representation model. With the belief rule structure, a data-driven belief rule base (DBRB) and a knowledge-driven belief rule base (KBRB) are learnt from uncertain training data and expert knowledge, respectively. A fusion algorithm is proposed to combine the DBRB and KBRB to obtain an optimal hybrid belief rule base (HBRB). A belief reasoning & decision making module is then developed to classify a query pattern based on the generated HBRB. An airborne target classification problem in the air surveillance system is studied to demonstrate the performance of the proposed HBRBCS for combining both uncertain sensor measurements and expert knowledge to make classification.

Mots clés: belief rule; Terms; Hybrid classification system; Index; uncertain data; expert knowledge

Domaines: Intelligence artificielle [cs.AI]

Lianmeng Jiao, Thierry Denoeux, Quan Pan. A Hybrid Belief Rule-Based Classification System Based on Uncertain Training Data and Expert Knowledge. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2016, 46 (12), pp.1711-1723. ⟨10.1109/TSMC.2015.2503381⟩. ⟨hal-01294273⟩